A Digital Twin Framework for Generation-IV Reactors with Reinforcement Learning-Enabled Health-Aware Supervisory Control
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Generation IV (Gen-IV) nuclear power plants are envisioned to replace the current reactor fleet, bringing improvements in performance, safety, reliability, and sustainability. However, large cost investments currently inhibit the deployment of these advanced reactor concepts. Digital twins bridge real-world systems with digital tools to reduce costs, enhance decision-making, and boost operational efficiency. In this work, a digital twin framework is designed to operate the Gen-IV Fluoride-salt-cooled High-temperature Reactor, utilizing data-enhanced methods to optimize operational and maintenance policies while adhering to system constraints. The closed-loop framework integrates surrogate modeling, reinforcement learning, and Bayesian inference to streamline end-to-end communication for online regulation and self-adjustment. Reinforcement learning is used to consider component health and degradation to drive the target power generations, with constraints enforced through a Reference Governor control algorithm that ensures compliance with pump flow rate and temperature limits. These input driving modules benefit from detailed online simulations that are assimilated to measurement data with Bayesian filtering. The digital twin is demonstrated in three case studies: a one-year long-term operational period showcasing maintenance planning capabilities, short-term accuracy refinement with high-frequency measurements, and system shock capturing that demonstrates real-time recalibration capabilities when change in boundary conditions. These demonstrations validate robustness for health-aware and constraint-informed nuclear plant operation, with general applicability to other advanced reactor concepts and complex engineering systems.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 67 references that are in the map. First posted on arXiv 9 Jun 2025.
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8 of 67 references.
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Same direction, newest first
- 2025Nuclear Microreactor Control with Deep Reinforcement Learning31 Mar
- 2024Design Optimization of Nuclear Fusion Reactor through Deep Reinforcement Learning12 Sep
Other references · 6
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2019Digital Twin: Values, Challenges and Enablerscited by 81
- 2022A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologiescited by 28
- 2023A digital twin framework for civil engineering structurescited by 10
- 2021Uncertainty Quantification and Software Risk Analysis for Digital Twins in the Nearly Autonomous Management and Control Systems: A Reviewcited by 5
- 2022Design of a Supervisory Control System for Autonomous Operation of Advanced Reactorscited by 3
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- 2025Digital Twin-enabled Multi-generation Control Co-Design with Deep Reinforcement Learning
- 2025Digital Twin Calibration with Model-Based Reinforcement Learning
- 2025Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics
- 2024A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants
- 2025Hybrid Modeling, Sim-to-Real Reinforcement Learning, and Large Language Model Driven Control for Digital Twins
- 2025Adaptive Sensor Steering Strategy Using Deep Reinforcement Learning for Dynamic Data Acquisition in Digital Twins
- 2026Foundation Twins: A New Generation of Power Systems Digital Twins using Foundation AI Models ↗
- 2026iFAN Ecosystem: A Unified AI, Digital Twin, Cyber-Physical Security, and Robotics Environment for Advanced Nuclear Simulation and Operations ↗